UNDERSTANDING THE DETERMINANTS OF FLUID INTAKE IN LONG-TERM CARE
Bibliographic record
Abstract
Dehydration is estimated to be present in almost half of long term care (LTC) residents, and many residents do not consume the recommended levels of daily fluid intake (3700mL and 2700mL in men and women respectively) (Institute of Medicine of the National Academies, 2004). This likely has negative consequences for health, well-being and quality of life. The present study aims to understand the factors contributing to fluid intake of LTC residents. Data were collected from 622 LTC residents (31.7% male) from 32 LTC homes in Canada, aged 62–107 years (86.8 ± 7.8). Total fluid intake was estimated over three non-consecutive days (meals and snacks), considering estimated volume of beverages and water content of liquidized food. Average daily fluid intake ranged from 311-2390mL (1103.9 ± 378.7). Rigorous methods were used to collect resident and unit-level variables that captured potential risk factors for low fluid intake such as dementia status, activities of daily living, eating challenges, and mealtime experiences. Hierarchical regression analysis using backward elimination revealed that fluid intake was negatively associated with increased age, cognitive impairment, eating challenges and increased dining room staffing. Factors that were positively associated with intake were: being male, requiring more physical assistance, and more positive interactions between staff and residents at meals (R2= 0.41; F88,533 = 4.20, p < 0.0001). These results indicate that total fluid intake of LTC residents is insufficient. Variables identified to predict intake could help inform strategies and targeted interventions to improve fluid intake for residents of LTC. Funded by the Canadian Institutes of Health Research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".